bulk-preprocess-data

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Last updated: 2024-04-03

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Knit directory: BITHub-preprocessing/

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Set-up

This R markdown document contains the complete code for the prep-rocessing of bulk RNA-sequencing files for Brain Integrative Transcriptome Hub (BITHub). All datasets were retrieved as described in Table 1 in the GitHub Repo.

In order to rerun the scripts - make sure to change the directories to the location of where the input files for pre-processing are stored. All directories are defined in code/preprocess/raw_file_locations.R. A copy of each processed metadata will also be saved in ’output/metadata/` for plotting purposes.

All other files are stored within the data folder of this R workflow directory.

library(recount3)
library(magrittr)
library(tibble)
library(reshape2)
library(SummarizedExperiment)
library(corrplot)
library(dplyr)
library(ggvenn)
library(pander)
library(gridExtra)
library(variancePartition)
library(DT)
library(EnsDb.Hsapiens.v86)
library(singscore)
library(AnnotationHub)
library(stargazer)
library(ggfortify)
library(glue)
library(cowplot)
library(broom)
library(glmpca)
library(DT)
library(naniar)
source("code/preprocess/functions.R")
source("code/preprocess/def_stages.R")
source("code/preprocess/raw_file_locations.R")

strc_acr <- map_df(structure_acronym, ~as.data.frame(.x), .id="id") %>% 
  set_colnames("StructureAcronym")
colnames(strc_acr)[2] = c("Structure")

rgns = map_df(regions, ~as.data.frame(.x), .id="id") %>% 
  set_colnames("Regions")
colnames(rgns)[2] = c("StructureAcronym")


rgns_ftl = map_df(regions_fetal, ~as.data.frame(.x), .id="id") %>% 
  set_colnames("Regions")
colnames(rgns_ftl)[2] = c("StructureAcronym")

Datasets to preprocess

BrainSeq

bseq.annot = read.csv(here::here("data/annotations/BrainSeq-metadata-annot.csv"), header = TRUE)
load(file.path(bseq, "rse_gene_unfiltered.Rdata"), envir = .GlobalEnv)
load(file.path(bseq,"methprop_pd.Rdata"), envir = .GlobalEnv)

x = rse_gene@colData 
x <- as.data.frame(x)
x <- as.data.frame(t(x))
replicated <- colnames(x)[grep(",", x["SAMPLE_ID",])]
y <- as.list(x)
y[replicated] <- lapply(y[replicated], function(z) {
  # which variables to merge
  to.weight <- which(sapply(z, length) > 1 & sapply(z, class) %in% c("numeric", "integer"))
  # weighting of the merge
  weighting <- z$numReads # total reads
  weighting <- weighting / sum(weighting) # rather than a straight average, it's based on the number of reads
  
  # apply weighting
  z[to.weight] <- lapply(z[to.weight], function(zz) {
    if (length(weighting) == length(zz)) {
      sum(weighting * zz)
      } else {
          NaN
        }
        
      })
      
      # quickly fix character variables
      char <- which(sapply(z, length) > 1 & sapply(z, class) == "character")
      z[char] <- lapply(z[char], function(zz) {
        paste(zz, collapse = " & ")
      })
      
      return(z)
    })
    
    w <- lapply(y, as.data.frame)
    w <- do.call("rbind", w)
    
    comp <- as.data.frame(pd)
    comp <- comp[,57:64]
    m <- match(rownames(comp), rownames(w)) # they are
    md<- cbind(w, comp[m,])
colnames(md) = bseq.annot$BITColumnName[match(colnames(md), bseq.annot$OriginalMetadataColumnName)]
    
    
    # Adding features 
md %<>% 
  rownames_to_column("SampleID") %>%
  mutate(Period = ifelse(.$AgeNumeric > 0, "Postnatal", "Prenatal"), 
                   StructureAcronym = gsub("HIPPO", "HIP", .$StructureAcronym),
                   Diagnosis = gsub("Schizo", "Schizophrenia", .$Diagnosis)) %>%
      mutate(Regions = add_feature(.$StructureAcronym, regions)) %>% 
      mutate(Age_rounded = as.character(sapply(na.omit(.$AgeNumeric), num_to_round))) %>% as.data.frame() %>%
      mutate(AgeInterval = as.character(add_feature(.$Age_rounded, age_intervals))) %>% 
      dplyr::select(-Age_rounded) %>%
      dplyr::select("SampleID", everything()) %>%
      as.data.frame()

md %>% 
  DT::datatable(caption = "BrainSeq Metadata after harmonizing column names, ages and structure acronyms")

exp = rse_gene@assays@.xData$data$rpkm
rownames(exp) <- sub("\\.[0-9]*$", "", rownames(exp))

Export

write.csv(exp, file = file.path(bseq.out, "BrainSeq-exp.csv"))
write.csv(md, file = file.path(bseq.out, "BrainSeq-metadata.csv"))
write.csv(md, file = file.path(here::here("data/metadata"), "BrainSeq-metadata.csv"))

BrainSpan

bspan.annot = read.csv(here::here("data/annotations/BrainSpan-metadata-annot.csv"), header = TRUE)

columns.metadata = read.csv(file.path(bspan, "columns_metadata.csv"), header = TRUE)
exp = read.csv(file.path(bspan, "expression_matrix.csv"), header= FALSE, row.names= 1)
rows.metadata = read.csv(file.path(bspan, "rows_metadata.csv"))

colnames(columns.metadata) = bspan.annot$BITColumnName[match(colnames(columns.metadata),bspan.annot$OriginalMetadataColumnName)]

md = columns.metadata %>% 
      mutate(Stage = add_feature(.$Age, stages), 
             AgeInterval = add_feature(.$Age, age_intervals), 
             Diagnosis = "Control", 
             Age = gsub(" ","_", .$Age)) %>%  
      mutate(SampleID = paste(DonorID, Age, StructureAcronym, Stage, sep = "_"), 
             age_for_mRIN = gsub("_", "", .$Age), 
             DonorName = gsub("\\.","_", .$DonorName),
            StructureAcronym = gsub("DFC", "DLPFC", StructureAcronym), 
            StructureAcronym = gsub("MFC", "ACC", StructureAcronym)) %>% 
  mutate(Regions = add_feature(.$StructureAcronym, regions)) %>%
      mutate('sample name' = paste(DonorName, age_for_mRIN,Sex ,StructureAcronym, sep = "//")) %>% 
      dplyr::select("SampleID", everything())

md$AgeNumeric[grepl("pcw", md$Age, ignore.case = TRUE)]<-
      md$Age[grepl("pcw", md$Age)] %>%  str_remove("_pcw")%>% 
      as.numeric() %>% `-` (40) %>% divide_by(52)
md$AgeNumeric[grepl("mos", md$Age, ignore.case = TRUE)] <- 
      md$Age[grepl("_mos", md$Age)] %>%  str_remove("_mos") %>% 
      as.numeric() %>% divide_by(12)
md$AgeNumeric[grepl("yrs", md$Age, ignore.case = TRUE)] <- 
      md$Age[grepl("_yrs", md$Age)] %>%  str_remove("_yrs") %>% 
      as.numeric 
    
md %<>% mutate(Period = ifelse(.$AgeNumeric >= 0, "Postnatal", "Prenatal"), 
                   colname = paste(DonorName, Age, StructureAcronym, sep = "_"))

colnames(exp) = md$SampleID
rownames(exp) <- rows.metadata$ensembl_gene_id

md.excel = read_excel(here::here("data/annotations/BrainSpan-additional.xlsx"),sheet =2, col_names = TRUE, skip =1) %>% 
      as.data.frame() %>% mutate_at(.vars = "AllenInstituteID", 
                                    .funs = gsub, pattern = "\\.", replacement = "\\_") %>%
      mutate_at(.vars = "Age", .funs = gsub, pattern = "PCW", replacement = "_pcw") %>% 
      mutate_at(.vars = "Age", .funs = gsub, pattern = "M", replacement = "_mos") %>%
      mutate_at(.vars = "Age", .funs = gsub, pattern = "Y", replacement = "_yrs") %>%
      mutate_at(.vars = "Region/Area", .funs = gsub, pattern = "\\/", replacement = "-") %>% 
      mutate(colname = paste(AllenInstituteID, Age, `Region/Area`, sep = "_")) %>% 
      dplyr::select(-c(Agerange, Age, Description))

md = md %>% 
      left_join(.,md.excel, by = "colname", keep = TRUE)

md.excel = read_excel(here::here("data/annotations/BrainSpan-additional.xlsx"), sheet = 1, col_names = TRUE) %>% 
      as.data.frame() %>% 
      mutate_at(.vars="Internal ID", .funs = gsub, pattern = "\\.", replacement = "\\_") %>% 
      dplyr::rename("Braincode" = "External ID")

md %<>% 
  left_join(md.excel, by ="Braincode") %>% 
      dplyr::select(-c("Age.y", "colname.y", "colname.x", "Gender", AllenInstituteID,
                     "Region/Area", "age_for_mRIN", "sample name", "Internal ID")) %>% 
      dplyr::rename("Ethnicity"="Ethn.")  %>% 
  distinct(SampleID, .keep_all = TRUE)


   
md = md[!duplicated(md[,c('column_num')]),]
md %<>% 
      dplyr::arrange(column_num)

md %>% 
  DT::datatable(caption = "BrainSpan metadata after metadata harmonization")

Export

write.csv(exp, file = file.path(bspan.out, "BrainSpan-exp.csv"))
write.csv(md, file = file.path(bspan.out,  "BrainSpan-metadata.csv"))
write.csv(md, file = file.path(here::here("data/metadata"), "BrainSpan-metadata.csv"))

GTEx

gtex.annot = read.csv(here::here("data/annotations/GTEx-metadata-annot.csv"), header = TRUE)

attributes = list.files(gtex, full.names = TRUE, pattern = "\\SampleAttributesDS.txt") # Sample attributes contains sample level information
phenotype = list.files(gtex, full.names = TRUE, pattern = "\\SubjectPhenotypesDS.txt") # Phenotype level information related to each donor 
exp = list.files(gtex, full.names = TRUE, pattern = "\\gene_tpm.gct.gz") # File used for expression matrix 
    
md.attrbutes = read_tsv(attributes, col_types = c('.default' = 'c')) %>% 
      dplyr::filter(SMTS == 'Brain') %>% 
      mutate(SUBJID = sapply(str_split(SAMPID, pattern = "-"), 
                             function(x) paste(x[1:2], collapse = '-'))) %>%
  left_join(read_tsv(phenotype, col_types = c('.default' = 'c')))  %>% 
  as.data.frame()
colnames(md.attrbutes) = gtex.annot$BITColumnName[match(colnames(md.attrbutes), gtex.annot$OriginalMetadataColumnName)]
    
 md.attrbutes %<>% 
  as.data.frame() %>% 
  mutate(StructureAcronym = add_feature(.$StructureAcronym, structure_acronym)) %>% 
      mutate(Regions = add_feature(.$StructureAcronym, regions), 
             AgeInterval = paste(.$AgeInterval, "yrs", sep = ""), 
             Diagnosis = "Control", 
             Sex = ifelse(Sex == 1, "M", "F"), 
             Period = "Postnatal") %>% as.data.frame()
    
exp = read.delim(exp, skip = 2)
colnames(exp) <- gsub("\\.", "-", colnames(exp)) # Changing expression file names to match metadata SampleIDs
exp %<>% column_to_rownames("Name")
    
exp = exp %>% 
      dplyr::select(contains(md.attrbutes$SampleID))
message(paste0("Samples subsetted - Exp matrix contains ", ncol(exp), " samples"))
rownames(exp) <- sub("\\.[0-9]*$", "", rownames(exp))
    
md.attrbutes= md.attrbutes[which(md.attrbutes$SampleID %in% colnames(exp)),] 

md.attrbutes %>% 
  DT::datatable(caption="GTEx metadata after preprocessing")

Export files

write.csv(exp, file = file.path(gtex.out, "GTEx-exp.csv"))
write.csv(md.attrbutes, file = file.path(gtex.out, "GTEx-metadata.csv"))
write.csv(md.attrbutes, file = file.path(here::here("data/metadata"), "GTEx-metadata.csv"))

Human developmental Biology Resource

Recount3 contains over 70,000 uniformly processed human RNA-seq samples. Recount provides gene, exon and exon-exon junction count matrices both in text format and as a RangedSummarizedExperiment.

The reads from recount were algined with the splice-aware Rail-RNA aligner. To compute the gene count matrices, the mapped reads were quantified with Gencode v25 with hg38 coordinates.

Unlike traditional quantification methods, recount3 provides base-pair coverage counts. Essentially, these are created in the following manner:

• For each exonic base-pair, the number of reads overlapping at that given base pair is computed. However, as library size is provided in recount3, the coverage counts can be scaled to read counts for a given library size.

hdbr = recount3::create_rse_manual(
  project = "ERP016243",
  type = "gene"
)

hdbr_annot = read.csv(here::here("data/annotations/HDBR-metadata-annot.csv"), header=TRUE)
hdbr_supp = read_excel("/home/neuro/Documents/BrainData/Bulk/HDBR/Amended Supplementary Table 1.xlsx", 
                       sheet = 1, skip =6) %>% 
  as.data.frame()

colnames(hdbr_supp) = c("DonorID","Age", "SampleID", "Structure","Hemisphere", "Sex", "PMI")
hdbr_supp %<>% dplyr::select("DonorID","Structure", "PMI")

hdbr_phenotype = read.csv("/home/neuro/Documents/BrainData/Bulk/HDBR/hdbr-phenotype.csv", 
                          header=TRUE)

load(here::here("data/annotations/HDBR.Rda"))

md.hdbr = md.hdbr[,1:15]

colnames(md.hdbr) = hdbr_annot$BITColumnName[match(colnames(md.hdbr), hdbr_annot$OriginalMetadataColumnName)]

md.hdbr %<>% 
  as.data.frame() %>% 
  dplyr::select(-c(Block, OntologyIndividual,  KaryotypeOntology, Organism, OrganismOntology, 
                   StructureOntology, HemisphereOntology, OntologyAge)) %>% 
  mutate(AgeInterval = add_feature(.$Age, age_intervals))  %>%
  dplyr::rename("Structure"= "StructureAcronym") %>%
  left_join(strc_acr, by = "Structure") %>% 
  left_join(rgns_ftl, by = "StructureAcronym") %>% 
  mutate(Sex = toupper(Sex)) %>% 
  mutate(Sex = ifelse(str_detect(Sex,"XX"), "F",
                      ifelse(str_detect(Sex, "XY"), "M", "Unknown" )), 
         Diagnosis = c("Control"), 
         Period = c("Prenatal"))

md = colData(hdbr) %>% 
  as.data.frame() %>% 
   dplyr::select(-contains("2"))
colnames(md) = hdbr_annot$BITColumnName[match(colnames(md), hdbr_annot$OriginalMetadataColumnName)]

md.hdbr %<>%
  mutate(Hemisphere = gsub("right", "R", Hemisphere), 
         Hemisphere = gsub("left", "L", Hemisphere), 
         Hemisphere = gsub("frontal", "Frontal", Hemisphere))

hdbr_supp %<>% 
  distinct(DonorID, .keep_all = TRUE) %>% 
  mutate(PMI = ifelse(PMI == "UNKNOWN", NA, PMI))
md.hdbr %<>% 
  left_join(hdbr_supp, by = c("DonorID"))
full.md.hdbr =left_join(md.hdbr, md)
full.md.hdbr %<>%
  mutate(PMI = as.numeric(PMI))

full.md.hdbr %<>% 
  dplyr::filter(Structure.x != "stomach")

assay(hdbr, "counts") = transform_counts(hdbr, round = TRUE)

hdbr.tpm = recount::getTPM(hdbr)

filter <- rowSums(hdbr.tpm, na.rm = TRUE) > 0.05
hdbr.filter.tpm = hdbr.tpm[filter,]
hdbr.filter.tpm <- thresh(hdbr.filter.tpm)

Export data

hdbr.final = hdbr.tpm %>% 
  as.data.frame() %>% 
  dplyr::select(contains(full.md.hdbr$SampleID)) %>%
  rownames_to_column("EnsemblID") %>% 
  mutate_at(.vars = "EnsemblID", .funs = gsub, pattern = "\\.[0-9]*$", replacement = "") 

write.csv(hdbr.final, file =file.path(hdbr.out, "HDBR-exp.csv"))

write.csv(full.md.hdbr, file =file.path(hdbr.out, "HDBR-metadata.csv"))
write.csv(full.md.hdbr, file = file.path(here::here("data/metadata"), "HDBR-metadata.csv"))

full.md.hdbr %>% 
  DT::datatable(caption="HDBR metadata after harmonization")

PsychEncode

pe.annot = read.csv(here::here("data/annotations/PsychEncode-metadata-annot.csv"), header=TRUE)


exp = list.files(psych, full.names = TRUE, pattern = "\\Gene_expression_matrix_TPM.txt") %>% 
      read.table(., header=TRUE, row.names = 1, check.names = FALSE)
md = list.files(psych, full.names = TRUE, pattern = "Job*") %>% read.csv(., header=TRUE) %>% 
  dplyr::filter(individualID != "2015-1")
md.clinical = read.csv(file.path(psych, "PEC_capstone_data_map_clinical.csv"), header=TRUE, check.names = FALSE)

comp = list.files(psych, full.names = TRUE, pattern = "\\Cell_fractions*") %>% read_excel() %>%
      as.data.frame() %>% 
      column_to_rownames("CellType")

colnames(md) = pe.annot$BITColumnName[match(colnames(md), pe.annot$OriginalColumnName)]
    # Fix existing columns 
comp = comp[,-1] 
comp = as.data.frame(t(comp))
m <- match(md$SampleID, rownames(comp))
md <- cbind(md, comp[m,])

    
# PCW to age numeric 
md$AgeNumeric[grepl("PCW", md$AgeNumeric, ignore.case = TRUE)] = md$AgeNumeric[grepl("PCW", md$AgeNumeric)] %>%
  str_remove("PCW")%>% 
  as.numeric() %>% `-` (40) %>% divide_by(52)
    
md$AgeNumeric = gsub("90+", "91", md$AgeNumeric)
md$AgeNumeric =gsub("\\+", "", md$AgeNumeric)
    
    
md$AgeNumeric <- as.numeric(as.character(md$AgeNumeric))
    
md %<>%
      dplyr::filter(Diagnosis == "Affective Disorder" |
               Diagnosis == "Autism Spectrum Disorder" | 
               Diagnosis == "Bipolar Disorder" |
               Diagnosis == "Control" |
               Diagnosis == "Schizophrenia") %>% 
      mutate(Structure = c("Dorsolateral Prefrontal Cortex"),  ## Adding name of structure
             StructureAcronym = c("DLPFC")) %>%  
      mutate(Period = ifelse(.$AgeNumeric >= 0, "Postnatal", "Prenatal"))  %>%
      mutate(Age_rounded = as.character(sapply(.$AgeNumeric, num_to_round))) %>% as.data.frame() %>%
      mutate(AgeInterval = as.character(add_feature(.$Age_rounded, age_intervals)),
             Death = as.character(add_feature(.$causeDeath, death_cause))) %>% 
      mutate(Regions = c("Cortex")) %>% 
      mutate(DonorID = as.character(.$SampleID)) %>%
      dplyr::select(-Age_rounded) %>%
      as.data.frame()


exp = exp %>%
  dplyr::select(contains(md$SampleID))

md = md %>% 
  dplyr::filter(SampleID %in% colnames(exp))
exp = exp[colnames(exp) %in% md$SampleID,] 
md = md[md$SampleID %in% colnames(exp),] 

Export data

write.csv(exp, file = file.path(pe.out, "PsychEncode-exp.csv"))
write.csv(md, file = file.path(pe.out,"PsychEncode-metadata.csv"))
write.csv(md, file = file.path(here::here("data/metadata"), "PsychEncode-metadata.csv"))

rm(md)
rm(exp)

Exploring dataset attributes

summarise_stats = function(x, dataset)
{
    age = table(x$AgeInterval) %>% melt()
    age$Type = c(paste(dataset, "Sample", sep = "_"))
    individuals = x %>% group_by(AgeInterval, DonorID) %>% 
        dplyr::summarise(n = n()) %>% 
        as.data.frame() 
    individuals = table(individuals$AgeInterval) %>% melt()
    individuals$Type = c(paste("Individual", dataset, sep = "_"))
    
    age = rbind(age, individuals)
    colnames(age) = c("AgeInterval", "n", "Type")
    age$dataset = c(as.character(dataset))
    return(age)
    
}

summarise_regions = function(x, dataset){
    regions = x %>% 
        dplyr::group_by(AgeInterval, Regions) %>% 
        dplyr::summarise(n = n())  %>% 
        as.data.frame() %>% 
        mutate(Dataset = dataset)
    return(regions)
}

age_interval_stats = list()
directory = file.path("/home/neuro/Documents/Brain_integrative_transcriptome/BITHub-preprocessing/data/metadata")
pattern = "/home/neuro/Documents/Brain_integrative_transcriptome/BITHub-preprocessing/data/metadata/"

for (f in directory){
    md = list.files(f, full.names = TRUE, pattern = "\\-metadata.csv$")
    
    for (j in md){
        ct_file = read.csv(j, header= TRUE)
        dataset = gsub(pattern, "", j)
        dataset = gsub("\\-metadata.csv","", dataset)
        message("Now calculating statistics for ", dataset)
        stats = summarise_stats(ct_file, dataset)
        age_interval_stats[[paste(dataset)]] = stats
    }
    
}


bulk_plot_version2 = age_interval_stats %>% 
    do.call(rbind, .) %>% 
    mutate(AgeInterval = factor(AgeInterval, levels = c("4-7pcw", "8-9pcw",
                                                        "10-12pcw", "13-15pcw", "16-18pcw",
                                                        "19-24pcw", "25-38pcw", "0-5mos",
                                                        "6-18mos", "19mos-5yrs", "6-11yrs",
                                                        "12-19yrs", "20-29yrs", "30-39yrs", "40-49yrs",
                                                        "50-59yrs", "60-69yrs", "70-79yrs", "80-89yrs", "90-99yrs")), 
           Type = factor(Type, levels = c("BrainSeq_Sample", "Individual_BrainSeq",
                                          "BrainSpan_Sample", "Individual_BrainSpan",
                                          "GTEx_Sample", "Individual_GTEx", 
                                          "HDBR_Sample", "Individual_HDBR", 
                                          "Ramakar_Sample", "Individual_Ramakar",
                                          "PsychEncode_Sample", "Individual_PsychEncode"))) %>%
    drop_na() %>%
    ggplot(aes(x= AgeInterval, y = n, fill =Type)) +
    geom_bar(position = "dodge",stat= "identity") + 
    facet_grid(dataset~AgeInterval, scales = "free") + xlab("") + ylab("")  + theme_bw() +
    theme(legend.position = "none", axis.text.x=element_blank(),
          strip.background =element_rect(fill="#AA9A9C", color = "#E1DFDB"), 
          panel.border = element_rect(color = "#f7f4ed", fill = NA, size = 2)) + 
    theme(strip.text = element_text(colour = 'white')) +
    scale_fill_manual(values = c("#F75E5E", "#FFC6BD",
                                 "#49165E", "#EBBAFF",
                                 "#78A2EB", "#36466A",
                                 "#74C69D", "#2D6A4F",
                                 "#F9AD79", "#FF5F0F")) + 
    theme(
        panel.background = element_rect(fill = "transparent",colour = NA), # or theme_blank()
        plot.background = element_rect(fill = "transparent",colour =NA)
    )


bulk_plot_version2 

#ggsave(width = 11.01, height = 9.30, units = "in", 
 #      file = "../../Results/exploratory/bulk_dist_thesis.svg", plot = bulk_plot_version2)
#ggsave(file = "../../Results/exploratory/bulk_dist_update.svg", bulk_plot, 
 #      height = 18.9624, width = 45.3501, units = "cm")

Session information

sessionInfo()

R version 4.3.3 (2024-02-29)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.4 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0

locale:
 [1] LC_CTYPE=en_AU.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_AU.UTF-8        LC_COLLATE=en_AU.UTF-8    
 [5] LC_MONETARY=en_AU.UTF-8    LC_MESSAGES=en_AU.UTF-8   
 [7] LC_PAPER=en_AU.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C       

time zone: Australia/Adelaide
tzcode source: system (glibc)

attached base packages:
 [1] tools     grid      stats4    stats     graphics  grDevices utils    
 [8] datasets  methods   base     

other attached packages:
 [1] lubridate_1.9.3             forcats_1.0.0              
 [3] purrr_1.0.2                 readr_2.1.5                
 [5] tidyverse_2.0.0             stringr_1.5.1              
 [7] tidyr_1.3.0                 scales_1.3.0               
 [9] data.table_1.14.10          readxl_1.4.3               
[11] naniar_1.0.0                glmpca_0.2.0               
[13] broom_1.0.5                 cowplot_1.1.2              
[15] glue_1.7.0                  ggfortify_0.4.16           
[17] stargazer_5.2.3             AnnotationHub_3.10.0       
[19] BiocFileCache_2.10.1        dbplyr_2.4.0               
[21] singscore_1.22.0            EnsDb.Hsapiens.v86_2.99.0  
[23] ensembldb_2.26.0            AnnotationFilter_1.26.0    
[25] GenomicFeatures_1.54.1      AnnotationDbi_1.64.1       
[27] DT_0.31                     variancePartition_1.32.2   
[29] BiocParallel_1.36.0         limma_3.58.1               
[31] gridExtra_2.3               pander_0.6.5               
[33] ggvenn_0.1.10               ggplot2_3.4.4              
[35] dplyr_1.1.4                 corrplot_0.92              
[37] reshape2_1.4.4              tibble_3.2.1               
[39] magrittr_2.0.3              recount3_1.12.0            
[41] SummarizedExperiment_1.32.0 Biobase_2.62.0             
[43] GenomicRanges_1.54.1        GenomeInfoDb_1.38.5        
[45] IRanges_2.36.0              S4Vectors_0.40.2           
[47] BiocGenerics_0.48.1         MatrixGenerics_1.14.0      
[49] matrixStats_1.2.0           workflowr_1.7.1            

loaded via a namespace (and not attached):
  [1] fs_1.6.3                      ProtGenerics_1.34.0          
  [3] bitops_1.0-7                  httr_1.4.7                   
  [5] numDeriv_2016.8-1.1           doRNG_1.8.6                  
  [7] backports_1.4.1               utf8_1.2.4                   
  [9] R6_2.5.1                      lazyeval_0.2.2               
 [11] withr_3.0.0                   prettyunits_1.2.0            
 [13] cli_3.6.2                     labeling_0.4.3               
 [15] sass_0.4.8                    mvtnorm_1.2-4                
 [17] recount_1.28.0                Rsamtools_2.18.0             
 [19] foreign_0.8-86                R.utils_2.12.3               
 [21] rentrez_1.2.3                 sessioninfo_1.2.2            
 [23] BSgenome_1.70.1               rstudioapi_0.15.0            
 [25] RSQLite_2.3.5                 generics_0.1.3               
 [27] BiocIO_1.12.0                 gtools_3.9.5                 
 [29] crosstalk_1.2.1               vroom_1.6.5                  
 [31] Matrix_1.6-5                  fansi_1.0.6                  
 [33] abind_1.4-5                   R.methodsS3_1.8.2            
 [35] lifecycle_1.0.4               whisker_0.4.1                
 [37] yaml_2.3.8                    edgeR_4.0.11                 
 [39] qvalue_2.34.0                 gplots_3.1.3                 
 [41] SparseArray_1.2.3             blob_1.2.4                   
 [43] promises_1.2.1                crayon_1.5.2                 
 [45] lattice_0.22-5                annotate_1.80.0              
 [47] KEGGREST_1.42.0               pillar_1.9.0                 
 [49] knitr_1.45                    rjson_0.2.21                 
 [51] boot_1.3-28.1                 corpcor_1.6.10               
 [53] codetools_0.2-19              getPass_0.2-4                
 [55] downloader_0.4                vctrs_0.6.5                  
 [57] png_0.1-8                     Rdpack_2.6                   
 [59] cellranger_1.1.0              gtable_0.3.4                 
 [61] cachem_1.0.8                  xfun_0.41                    
 [63] rbibutils_2.2.16              S4Arrays_1.2.0               
 [65] mime_0.12                     iterators_1.0.14             
 [67] statmod_1.5.0                 interactiveDisplayBase_1.40.0
 [69] ellipsis_0.3.2                nlme_3.1-164                 
 [71] pbkrtest_0.5.2                bit64_4.0.5                  
 [73] progress_1.2.3                EnvStats_2.8.1               
 [75] filelock_1.0.3                rprojroot_2.0.4              
 [77] GenomicFiles_1.38.0           bslib_0.6.1                  
 [79] rpart_4.1.23                  KernSmooth_2.23-22           
 [81] Hmisc_5.1-1                   colorspace_2.1-0             
 [83] DBI_1.2.1                     nnet_7.3-19                  
 [85] tidyselect_1.2.0              processx_3.8.3               
 [87] bit_4.0.5                     compiler_4.3.3               
 [89] curl_5.2.0                    git2r_0.33.0                 
 [91] graph_1.80.0                  htmlTable_2.4.2              
 [93] derfinder_1.36.0              xml2_1.3.6                   
 [95] DelayedArray_0.28.0           rtracklayer_1.62.0           
 [97] checkmate_2.3.1               caTools_1.18.2               
 [99] remaCor_0.0.16                callr_3.7.3                  
[101] rappdirs_0.3.3                digest_0.6.34                
[103] minqa_1.2.6                   rmarkdown_2.25               
[105] GEOquery_2.70.0               aod_1.3.3                    
[107] XVector_0.42.0                RhpcBLASctl_0.23-42          
[109] base64enc_0.1-3               htmltools_0.5.7              
[111] pkgconfig_2.0.3               lme4_1.1-35.1                
[113] highr_0.10                    fastmap_1.1.1                
[115] rlang_1.1.3                   htmlwidgets_1.6.4            
[117] shiny_1.8.0                   farver_2.1.1                 
[119] jquerylib_0.1.4               jsonlite_1.8.8               
[121] R.oo_1.25.0                   VariantAnnotation_1.48.1     
[123] RCurl_1.98-1.14               Formula_1.2-5                
[125] GenomeInfoDbData_1.2.11       munsell_0.5.0                
[127] Rcpp_1.0.12                   visdat_0.6.0                 
[129] stringi_1.8.3                 zlibbioc_1.48.0              
[131] MASS_7.3-60.0.1               bumphunter_1.44.0            
[133] plyr_1.8.9                    parallel_4.3.3               
[135] Biostrings_2.70.1             splines_4.3.3                
[137] hms_1.1.3                     derfinderHelper_1.36.0       
[139] locfit_1.5-9.8                ps_1.7.6                     
[141] rngtools_1.5.2                biomaRt_2.58.0               
[143] BiocVersion_3.18.1            XML_3.99-0.16                
[145] evaluate_0.23                 BiocManager_1.30.22          
[147] foreach_1.5.2                 nloptr_2.0.3                 
[149] tzdb_0.4.0                    httpuv_1.6.13                
[151] xtable_1.8-4                  restfulr_0.0.15              
[153] fANCOVA_0.6-1                 later_1.3.2                  
[155] lmerTest_3.1-3                memoise_2.0.1                
[157] GenomicAlignments_1.38.2      cluster_2.1.6                
[159] timechange_0.3.0              GSEABase_1.64.0              
[161] here_1.0.1